The studies in the thesis have been conducted to provide reasonable evidence to validate enhanced cognitive function through a combination of exogenous and endogenous stimulations. Following a review of the research literature and theoretical thought on the application and understanding of SMR neurofeedback training and electrical acustimulation protocols, it is possible to identify three major goals for three experiments, in accordance with the literature examined.
97 Exp I – Beneficial effects of electrostimulation contingencies on sustained attention and electrocortical activity
With regard to the change in sustained attention, an improvement in behavioural results (perceptual sensitivity) and their ERP is modulated by real EA stimulation with specific frequency (alternating frequency vs. low frequency vs. sham stimulation). Certainly, whether or not traditional EEG and ERP methods show significant changes in electrophysiology, the ICA-based EEG analysis provides significant results in the EEG and ERP studies.
The aim of the first experiment in the thesis is also to compare the results of the stimuli-produced cortical activities for three conditions (before, during and after EA stimulation), to identify whether the attentional ERPs and performance are altered by EA stimulation, even in the period of post stimulation (the outlasting effect after EA stimulation). Meanwhile, the presumed components, reflecting synchronous cortical local field activity of connected networks, can be decomposed from ERP data, via ICA decomposition, using spatial filters for each group and each time period. Therefore, based on the results of the experiment, EA can be used in the later experiment as an assisting modality in the SMR NF training.
The ICA method for the analysis of EEG data is a very important issue, not only for ERP, but also for the existing resting state EEG networks. Furthermore, the idea from previous studies indicates that the general effect of NFT may be better described by its action on the resting EEG (Egner, et al., 2004; Ros, et al., 2010), which is
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highly correlated with the dorsal attention network (Laufs, 2008; Mantini, et al., 2007; Uddin, et al., 2009). Therefore, in order to provide more evidence of enhanced attentional performance, using NFT and NFT assisted by EA, the following experiment focuses on exploring the default and attentional networks. In order to improve the application of ICA, the methods of the second experiment serve to validate that any improvement to the attention network is correlated with trained oscillations (in the third experiment).
99 Exp II – Dynamic changes of ICA-derived EEG functional connectivity in the resting state
EEG epochs, as fMRI volumes of individual-subject data, can be concatenated across subjects, along the time axis to apply the ICA algorithm to group data. Furthermore, combining ICA with time-frequency and cross-correlation analyses performed on the power spectra of selected ICs at the group-level reveals information about resting EEG networks, with regard to neural synchronization (Chen et al., 2009;
Grin-Yatsenko, et al., 2010).
This experiment focuses on the steps to model and examine the effective resting EEG networks established by similarity in the components’ alpha power, in order to investigate: (a) the topographical maps of EEG components in both EC and EO states; (b) the associated EEG sources according to their alpha power correlation coefficients in both states; (c) the localization of circumscribed groups associated with relevant EEG components, from the EC to the EO state; (d) the alpha power-associated functional connectivity between ICs and the difference between EC and EO states and (e) the changes in spectral power in the circumscribed groups, from the EC to the EO state. Then, based on the previous two experiments’ results, the third experiment investigates the effect of a combination of NF training and EA stimulation on the attention performance, the enhanced and inhibited oscillation after NFT and the improvement in spectral power, within circumscribed regions of attention network.
100 Exp III –The increased perceptual sensitivity in attention performance and the enhanced beta power of the attention network in the resting state caused by a combination of neurofeedback self-regulation and electroacupuncture stimulation.
It is plausible to utilize the lasting effect of post-EA stimulation outlasting to boost the improvement in attention performance, while undertaking NF training to increase SMR and decrease theta activity, during the post-EA interval. This improvement does not occur in the non-contingent (sham feedback) group. However, superior cognitive benefits result from NF training assisted by EA stimulation, as validated by the increase in regional attention-related spectral power of the formerly developed EEG attention network.
Finally ICA-based EEG power spectra are used to study the differences between pre- and post- NF training. Comprehensive data for the identified EEG components and networks is collected, to identify the source of differences in attentional performance between the four groups (AE+SMR, LE+SMR, SMR, and non-contingent SMR). To the best of the author's knowledge, no studies have specifically investigated a potential improvement in attentional performance and the EEG dynamics of the dorsal attention network, due to a combination of NFT and EA. Importantly, no study to date has studied the differences in attentional performance due to SMR and the non-contingent SMR (pseudo-feedback) NF training, or in EEG dynamics. The third experiment attempts to validate the possible long-term effects of NF training.
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